NEIGHBORHOOD FUNCTION DESIGN FOR EMBEDDING IN REDUCED DIMENSION

Jiun-Wei Liou, Cheng-Yuan Liou

Abstract

LLE(Local linear embedding) is a widely used approach for dimension reduction. The neighborhood selection is an important issue for LLE. In this paper, the e-distance approach and a slightly modified version of k-nn method are introduced. For different types of datasets, different approaches are needed in order to enjoy higher chance to obtain better representation. For some datasets with complex structure, the proposed Ɛ-distance approach can obtain better representations. Different neighborhood selection approaches will be compared by applying them to different kinds of datasets.

References

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Paper Citation


in Harvard Style

Liou J. and Liou C. (2011). NEIGHBORHOOD FUNCTION DESIGN FOR EMBEDDING IN REDUCED DIMENSION . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 190-195. DOI: 10.5220/0003681201900195


in Bibtex Style

@conference{ncta11,
author={Jiun-Wei Liou and Cheng-Yuan Liou},
title={NEIGHBORHOOD FUNCTION DESIGN FOR EMBEDDING IN REDUCED DIMENSION},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={190-195},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003681201900195},
isbn={978-989-8425-84-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - NEIGHBORHOOD FUNCTION DESIGN FOR EMBEDDING IN REDUCED DIMENSION
SN - 978-989-8425-84-3
AU - Liou J.
AU - Liou C.
PY - 2011
SP - 190
EP - 195
DO - 10.5220/0003681201900195